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Н Новости

RLHF. История становления идеи — 2

В прошлой части мы

верхнеуровнево познакомились с общим пайплайном RLHF,
повторили за старшими товарищами аналогию "Шоггота с улыбающимся лицом",
посмотрели на создание неукротимого монстра (LLM),
упомянули необходимость предобучения на пусть и грязных, но больших данных,
а также успели дополнить технику наивного RL KL-контролем.

В этой части посмотрим на обучение RL сразу поверх предобученной модели. Пробуем создать красивую масочку и натянуть ее на неукротимого монстра.

Глава 3 (RLHF для языка и Offline RL), в которой мы первый раз и пока наивно пробуем применить техники предобучения, KL-контроля и сбора человеческой обратной связи для обучения языковой модели. Задаемся вопросами о сборе человеческой обратной связи, понимаем, что не всякая обратная связь одинаково полезна, а еще опасаемся применять Online RL. (Human-centric Dialog Training via Offline Reinforcement Learning (Jaques et al. 2019))

Глава 4 (Self-play), в которой мы осознаем преимущества и ограничения Offline RL, пробуем обойти негативные стороны Offline и Online RL и имитировать Online RL с помощью техники общения модели самой с собой. (Hierarchical Reinforcement Learning for Open-Domain Dialog (Saleh et al. 2019))

Глава 5 (Reward Model), в которой мы все еще бьемся с ограничениями Offline RL, избегаем Online RL, расширяем пространство доступных вознаграждений с помощью обучения дополнительной модели вознаграждений и еще раз задумываемся о виде человеческой обратной связи для обучения модели вознаграждений. (Deep reinforcement learning from human preferences (Christiano et al. 2017))

Глава 3. RLHF для языка и Offline RL

Jaques et al. 2019, Human-centric Dialog Training via Offline Reinforcement Learning

Как использовать общий подход RL для обучения с использованием человеческой обратной связи?

Когда мы добавляем к аббревиатуре RL еще две буквы HF, это, по факту, означает, что мы заменяем явно заданную функцию вознаграждения (reward function, RF) в пайплайне RL на обратную связь от человека (human feedback, HF), который теперь оценивает качество выбранного агентом действия.

Было:

что тут происходит, было описано в предыдущей части

Стало:

Какой вид обратной связи от человека подойдет для обучения RL агента?

Некоторая интуиция подсказывает нам с самого начала отказаться от идеи сбора простых ручных меток. Они могут оказаться не самой "богатой" и качественной обратной связью: будут ошибочны из-за мискликов, несогласованы между разными разметчиками, для меток сложно подобрать подходящую шкалу оценки, принятую всеми разметчиками, сложно подобрать критерии для такой шкалы и выразить в них все наши пожелания к поведению модели и т.д. Может быть, мы хотим использовать что-то другое? Например, социальную обратную связь? Это весьма богатый сигнал, который люди получают из своего окружения постоянно. Если говорить в контексте сбора обратной связи для обучения автономного домашнего робота, можно попросить человека просмотреть траектории этого робота и пометить их как хорошие или плохие. А можно наблюдать за социальными сигналами человека: языком тела, жестами, тоном голоса. Такие сигналы можно получать непрерывно, в отличие от ручного ввода сигналов, такая обратная связь лучше масштабируется. Получается обучение с человеком в цикле (human-in-the-loop) без дополнительных усилий со стороны человека.

Как собирать такую социальную обратную связь в контексте языковых моделей?

С помощью живого взаимодействия модели с пользователями и оценки сентимента (настроения) ответов человека при общении с моделью в диалоге. Кажется, что так можно понять, когда человек доволен или недоволен общением с ботом, и чем конкретно человек не доволен. Такой диалог естественным и пассивным образом дает подсказки о том, как протекает разговор, что бот делает хорошо, а что плохо.

В рамках разработки самой функции вознаграждения, авторы статьи
1) свели к минимум сбор ручных оценок диалогов человеком, чтобы не нарушать естественное течение разговора
2) сосредоточились на сборе пассивных сигналов из сентиментов (настроения) разговора
3) изучили литературу не только по диалоговым моделям, но и по человеческим разговорам и эмпатии в разговорах, и выработали гипотетический набор сигналов, на которых можно было бы обучаться - (1) умение вызывать положительные эмоции у собеседника, (2) смех, (3) не повторяться, (4) оставаться в теме разговора и (5) задавать вопросы.

картинка отсюда

Откуда собирать социальную и достаточно разнообразную обратную связь?

Разместить этих агентов в Интернете и дать им возможность общаться с людьми в сети? Но это на самом деле довольно опасно. Мы помним, что может произойти, если модель постоянно обновляется на основе обратной связи, которую она получает от пользователей в сети. Наши агенты могут также очень быстро научиться неуместному поведению (TayTweets, @TayandYou).

Итак, с одной стороны нужно иметь возможность тщательно протестировать модель перед развертыванием, а с другой - мы будто бы не можем постоянно учиться в сети. Так появляется разорванный цикл взаимодействия, где мы не можем исследовать обратную связь непрерывно онлайн. Нужно как-то использовать RL для обучения на статичных данных без возможности непрерывного исследования среды. Эта парадигма называется Offline RL и сама по себе является крайне нетривиальной задачей.

Offline RL

В чем проблема RL без доступа к исследованию среды? В том, что существуют некоторые области пространства среды, которые агент никогда не может посетить, чтобы исследовать и чтобы узнать значение функции вознаграждения в этих областях.

Если в качестве статичных данных доступна некоторая область пространства среды (желтая область на картинке выше), агент никогда не увидит состояние среды в белых областях во время обучения. Но в инференсе, ничто не ограничивает агента от перехода из желтых областей в белые. RL-агент оптимистичен в условиях неопределенности - т.е. он ожидает большего вознаграждения от попадания в неизвестную (белую на картинке выше) область. Поэтому он может переоценить ценность такого перехода, эта переоценка будет также распространяться и на предшествующие действия, что приведет к накоплению неограниченной ошибки в оценке ценности определенных действий и состояний.

Можно попробовать контролировать такой переход из желтых областей в белые области неопределенности с помощью техники копирования самого частого поведения из данных (behavioral cloning, красная линия на картинке выше). Но это не всегда будет являться оптимальной траекторией (на самом деле, на картинке оптимальная траектория проходит по низу карты).

Тут снова на выручку приходит KL-контроль. Только теперь нашим приором будет не модель (как было в главе 2), а сами статические данные и вероятности соответствующих действий из этих данных.

Как и в прошлый раз, KL-контроль помогает победить коллапс мод (mode collapse, коллапс режимов) - эффект, когда модель "застревает" на определенных шаблонах или стилях текста, игнорируя остальную часть возможного пространства ответов. Если вознаграждать модель за количество заданных пользователю вопросов, наивный RL также наивно использует функцию вознаграждения и отклонит модель от естественного языка. С использованием KL-контроля модель остается реалистичной, продолжает использовать естественный язык, но для оптимизации функции вознаграждения становится более дружелюбной, вежливой и поддерживающей (авторы шутят, называя такое поведение модели "канадским").

картинка отсюда

В качестве хорошего бонуса авторы смогли подтвердить гипотезу, что оптимизация на основе однозначных бинарных ручных меток оценок диалогов "хорошо/плохо", несмотря на то, что такие метки должны являться хорошим и прямым сигналом мнения людей, масштабируется не так хорошо, как оптимизация на основе неявной социальной обратной связи.

Однако в этой работе также обнаружилась проблема: не смотря на то, что поведение модели, очевидно, стало более дружелюбным и вежливым, это не привело к более высоким оценкам качества разговора по мнению людей. Что происходит?

Это случай, когда обнаруживается некий "налог на выравнивание"? Оптимизируя функцию вознаграждения, модель действительно стала более вежливой, но это не то, что нужно людям на самом деле, и поэтому она не начала нравится людям больше?
Или выбранная функция вознаграждения неверна и не описывает "хороший разговор"?
Или это происходит из-за того, что исследование проводилось в академическом масштабе, были оплачены и собраны только несколько тысяч оценок людей (а это совсем не миллионы данных, которые использует OpenAI), так что просто не достаточно данных для обучения или для того, чтобы сделать корректный вывод о качестве модели?

Чтобы разобраться, попробуем уйти от режима Offline RL.

Глава 4. Self-play

Saleh et al. 2019, Hierarchical Reinforcement Learning for Open-Domain Dialog

Чтобы имитировать Online RL в безопасной среде, заменяем пользователя, который общается с обучаемой моделью, на другую "замороженную" модель и используем сентимент (настроение) текста, сгенерированного другой моделью, в качестве вознаграждения. Интересно, что в таком сетапе модель действительно достигает лучших оценок качества от людей по сравнению с базовыми языковыми моделями, а также по сравнению с другими базовыми RL подходами и даже моделью из предыдущей работы авторов.

таблица отсюда

Batch $\Psi$ = модель из предыдущей работы авторов (и из нашей Главы 3)
Transformer = Transformer из Attention Is All You Need (Vaswani et al. 2017)
VHRED = Variational Hierarchical Recurrent Encoder Decoder (Serban et al. 2017)
REINFORCE = наивный агент, учится на self-play
VHRL (Variational Hierarchical Reinforcement Learning) = VHRED + REINFORCE

Кажется, что ответ на вопрос предыдущей главы - все такие не хватило данных. Агент, получивший доступ к обширной обратной связи, не ограниченной только собранным датасетом, справляется лучше. Эта гипотеза еще несколько раз эмпирически подтвердится и в последующих работах.

Можно ли использовать режим self-play бесконечно и ожидать, что модели будут улучшать свои способности также бесконечно?

Мы об этом узнаем только в главе 7. Но спойлер тут тоже упомянем: оказывается, что нет. Со временем модели начнут подстраиваться под функции вознаграждения друг друга и человеческие оценки их качества начнут падать.

Какая есть еще причина отдавать предпочтение в подобных задачах обучения LLM в пользу Offline RL по сравнению с Online RL (кроме уже упомянутой опасности из-за отсутствия контроля за обратной связью получить по итогу модель, обученную неподобающему поведению)?

1) Достаточность собранной обучающей выборки (в открытом доступе сегодня достаточно данных человеческой обратной связи, чтобы можно было позволить себе не тратиться на дополнительные разметки).
2) Простота с точки зрения инфраструктуры. Создание инфраструктуры для RLHF в крупном масштабе, как это, например, делает OpenAI, — это, мягко говоря, нетривиальная задача.
3) Возможность повторного использования конвеера обучения для новых экспериментов с новыми мета-параметрами. Для проведения нового эксперимента полного RLHF в сетапе Online RL придется, по сути, создавать новую среду для агента, нанимать новых разметчиков и т.д.

Можно ли как то еще обойти ограничения Offline и Online RL и одновременно использовать преимущества обоих подходов?

Глава 5. Reward Model

Christiano et al. 2017, Deep reinforcement learning from human preferences

Тут мы сделаем исторический шаг назад и вернемся в 2017. RLHF, как и любой другой RL начинался, конечно же, не с языковых моделей, а с игр. Начинаем почти заново с вопроса о том, что модели RL крайне прожорливы в смысле обучающих данных, а сбор обратной связи от людей - достаточно дорогая процедура.

Как можно решить проблему? Если запрос обратной связи от человека - это дорого, к чему еще можно сделать запрос об обратной связи?

Модель вознаграждений (Reward model).

Можно обучить модель вознаграждений на основе человеческой обратной связи. Эту модель можно будет опрашивать столько, сколько нужно (прям как в сетапе с self-play) и бесплатно. Теперь возможно выполнять весь цикл RL в симуляции, где модель вознаграждений заменяет человека.

Да, все еще нужно будет собрать достаточно много данных от людей для обучения качественной модели вознаграждений. И почему тогда все еще лучше обучать именно ее, как какой-то промежуточный этап, а не использовать достаточно объемную базу человеческих оценок для непосредственного обучения агента RL?

Потому что ожидается, что модель вознаграждений сможет лучше использовать разреженные человеческие оценки. Эмпирически, обучение с учителем лучше интерполирует и генерализирует на меньшем объеме данных, по сравнению с RL. Таким образом, мы ожидаем, что обученная с учителем модель вознаграждений поможет интерполировать точки данных, обобщив свои знания в инференсе и на неисследованные области данных. Бонусом, получим некоторую устойчивость против коллапса мод.

Так в пайплайн RLHF добавляется новый элемент - модель вознаграждений, вместо явной функции вознаграждений или явной человеческой обратной связи.

Было

Стало

Какой вид обратной связи нужно получать от людей и как собрать действительно качественные данные?

Люди на самом деле довольно плохо умеют давать обратную связь, особенно, когда запрашиваемые оценки выражены в каких то абсолютных единицах. Но вот задача сравнения двух образцов уже решается человеком надежнее и с большей вероятностью позволяет получить консистентные оценки от большого количества разметчиков. В таком сетапе предпочтения выбора между двумя образцами выражаются в виде 0 и 1, если какой-то образец предпочтительнее другого, либо в виде 0.5 и 0.5, если образцы равнозначны. Модель вознаграждений учится на этих данных имитировать человеческие оценки, а дальше эксплуатируется агентом в процессе RL. Важная деталь к вопросу сбора обратной связи, которую тут стоить отметить - это то, что авторы явно учитывают вероятность 10% ошибки (мисклик) собранной обратной связи в функции потерь при обучении модели вознаграждений.

Теперь агент обучается предпочитать одну траекторию другой, если суммарные вознаграждения одной траектории выше другой.

В своей работе авторы исследуют и сравнивают результаты обучения RL в трех сценариях:

с помощью явно сформулированной функции вознаграждения от среды MuJoCo, Arcade Learning Environment и OpenAI Gym (оранжевый цвет на картинке ниже)
с помощью явно переданной человеческой обратной связи в виде Offline RL (фиолетовый цвет на картинке ниже)
с помощью модели вознаграждений (RM), обученной на человеческих метках (спектр синего цвета на картинке ниже)

картинка отсюда. Разные графики - это разные игры из симуляции.

Агент, обученный на явных человеческих оценках (фиолетовый цвет на картинке выше) в большинстве случаев справляется хуже, чем агент, обученный с помощью явной функции вознаграждения от среды (оранжевый цвет на картинке выше). Что будто бы подтверждает теорию о том, что наивного обучения в лоб только в пространстве собранных меток от людей - недостаточно, чтобы качественно обучить агента.

А вот агент, обученный на модели вознаграждений (спектр синего цвета на картинке выше), справляется либо так же хорошо, либо иногда лучше, чем агент, обученный на функции вознаграждений от среды (оранжевый цвет на картинке выше). Т.е. имитация сигнала приближается по "богатству" сигнала к полному пространству вознаграждения от среды.

Так мы получили подтверждение, что модель вознаграждений (даже обученная на не очень большом количестве собранных меток) работает как минимум с той же точностью, что и явное вознаграждение от среды. Как следствие - получили возможность обучать агентов на качественном вознаграждении даже в ситуации, когда у нас нет возможности явно задать функцию вознаграждения.

Итого, наш сетап на данный момент содержит следующие инструменты:

предобучение, KL-контроль, непротиворечивая человеческая обратная связь, модель человеческих предпочтений.

Можно ли лучше? Что бы еще добавить? - Лучше можно, и весь предыдущий опыт МЛ разработки говорит нам, что добавить надо больше обучения богу обучения. Как? - Ответ тоже из эмпирического опыта - больше качественных данных и больше обучения с учителем (Supervised finetune).

Куда это нас приведет, мы узнаем в Главе 6.

Mrs Wallbreaker or: How I Learned to Stop Worrying and Love the AGI.
About AI Risk, AI Alignment, AI Safety, AI Ethics
https://t.me/MrsWallbreaker

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26 июл. 2024 г.

07.09.23 16:24 CherryTeam

Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/
08.10.23 01:30 davec8080

The "Shibarium for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH. The Plot Thickens. Someone posted the actual transactions!!!! https://bscscan.com/tx/0xa846ea0367c89c3f0bbfcc221cceea4c90d8f56ead2eb479d4cee41c75e02c97 It seems the article is true!!!! And it's also FUD. Let me explain. Check this link: https://bscscan.com/token/0x5a752c9fe3520522ea88f37a41c3ddd97c022c2f So there really is a "Shibarium" token. And somebody did a rug pull with it. CONFIRMED. But the "Shibarium" token for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH.
24.06.24 04:31 tashandiarisha

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26.06.24 18:46 Jacobethannn098

LEGAL RECOUP FOR CRYPTO THEFT BY ADRIAN LAMO HACKER
26.06.24 18:46 Jacobethannn098

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04.07.24 04:49 ZionNaomi

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13.07.24 21:13 michaelharrell825

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17.07.24 02:26 thompsonrickey

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18.07.24 20:13 austinagastya

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27.08.24 12:50 James889900

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27.08.24 13:06 James889900

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02.09.24 20:24 [email protected]

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06.09.24 01:35 Celinagarcia

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06.09.24 01:44 Celinagarcia

HOW TO RECOVER MONEY LOST IN BITCOIN/USDT TRADING OR TO CRYPTO INVESTMENT !! Hi all, friends and families. I am writing From Alberton Canada. Last year I tried to invest in cryptocurrency trading in 2023, but lost a significant amount of money to scammers. I was cheated of my money, but thank God, I was referred to Hack Recovery Wizard they are among the best bitcoin recovery specialists on the planet. they helped me get every penny I lost to the scammers back to me with their forensic techniques. and I would like to take this opportunity to advise everyone to avoid making cryptocurrency investments online. If you have already lost money on forex, cryptocurrency or Ponzi schemes, please contact [email protected] or WhatsApp: +1 (757) 237–1724 at once they can help you get back the crypto you lost to scammers. BEST WISHES. Celina Garcia.
16.09.24 00:10 marcusaustin

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16.09.24 00:11 marcusaustin

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23.09.24 18:56 matthewshimself

At first, I was admittedly skeptical about Worldcoin (ref: https://worldcoin.org/blog/worldcoin/this-is-worldcoin-video-explainer-series), particularly around the use of biometric data and the WLD token as a reward mechanism for it. However, after following the project closer, I’ve come to appreciate the broader vision and see the value in the underlying tech behind it. The concept of Proof of Personhood (ref: https://worldcoin.org/blog/worldcoin/proof-of-personhood-what-it-is-why-its-needed) has definitely caught my attention, and does seem like a crucial step towards tackling growing issues like bots, deepfakes, and identity fraud. Sam Altman’s vision is nothing short of ambitious, but I do think he & Alex Blania have the chops to realize it as mainstay in the global economy.
01.10.24 14:54 Sinewclaudia

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02.10.24 22:27 Emily Hunter

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
18.10.24 09:34 freidatollerud

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31.10.24 00:13 ytre89

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
02.11.24 14:44 diannamendoza732

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12.11.24 00:50 TERESA

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17.11.24 09:31 Vivianlocke223

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19.11.24 03:06 [email protected]

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19.11.24 03:07 [email protected]

My entire existence fell apart when a malevolent hacker recently gained access to my online accounts. I felt violated and extremely uneasy after discovering that the digital platforms I depended on for communication, employment, and finances had been compromised. Regaining control and restoring my digital security was an overwhelming task in the immediate aftermath. To help me navigate the difficult process of recovering my accounts and getting my peace of mind back, TRUST GEEKS HACK EXPERT came into my life as a ray of hope. They immediately put their highly skilled professionals to work, thoroughly examining the vulnerability and methodically preventing unwanted access. They guided me through each stage soothingly, explaining what was occurring and why, so I never felt lost or alone. They communicated with service providers to restore my legitimate access while skillfully navigating the complex labyrinth of account recovery procedures. My digital footprint was cleaned and strengthened against future attacks thanks to their equally amazing ability to remove any remaining evidence of the hacker's presence. However, TRUST GEEKS HACK EXPERT actual worth went beyond its technical aspects. They offered constant emotional support during the ordeal, understanding my fragility and sense of violation. My tense nerves were calmed by their comforting presence and kind comments, which served as a reminder that I wasn't alone in this struggle. With their help, I was able to reestablish my sense of security and control, which enabled me to return my attention to the significant areas of my life that had been upended. Ultimately, TRUST GEEKS HACK EXPERT all-encompassing strategy not only recovered my online accounts but also my general peace of mind, which is a priceless result for which I am incredibly appreciative of their knowledge and kindness. Make the approach and send a message to TRUST GEEKS HACK EXPERT Via Web site <> www://trustgeekshackexpert.com/-- E>mail: Trustgeekshackexpert(At)fastservice..com -- TeleGram,<> Trustgeekshackexpert
21.11.24 04:14 ronaldandre617

Being a parent is great until your toddler figures out how to use your devices. One afternoon, I left my phone unattended for just a few minutes rookie mistake of the century. I thought I’d take a quick break, but little did I know that my curious little genius was about to embark on a digital adventure. By the time I came back, I was greeted by two shocking revelations: my toddler had somehow managed to buy a $5 dinosaur toy online and, even more alarmingly, had locked me out of my cryptocurrency wallet holding a hefty $75,000. Yes, you heard that right a dinosaur toy was the least of my worries! At first, I laughed it off. I mean, what toddler doesn’t have a penchant for expensive toys? But then reality set in. I stared at my phone in disbelief, desperately trying to guess whatever random string of gibberish my toddler had typed as a new password. Was it “dinosaur”? Or perhaps “sippy cup”? I felt like I was in a bizarre game of Password Gone Wrong. Every attempt led to failure, and soon the laughter faded, replaced by sheer panic. I was in way over my head, and my heart raced as the countdown of time ticked away. That’s when I decided to take action and turned to Digital Tech Guard Recovery, hoping they could solve the mystery that was my toddler’s handiwork. I explained my predicament, half-expecting them to chuckle at my misfortune, but they were incredibly professional and empathetic. Their confidence put me at ease, and I knew I was in good hands. Contact With WhatsApp: +1 (443) 859 - 2886 Email digital tech guard . com Telegram: digital tech guard recovery . com website link :: https : // digital tech guard . com Their team took on the challenge like pros, employing their advanced techniques to unlock my wallet with a level of skill I can only describe as magical. As I paced around, anxiously waiting for updates, I imagined my toddler inadvertently locking away my life savings forever. But lo and behold, it didn’t take long for Digital Tech Guard Recovery to work their magic. Not only did they recover the $75,000, but they also gave me invaluable tips on securing my wallet better like not leaving it accessible to tiny fingers! Who knew parenting could lead to such dramatic situations? Crisis averted, and I learned my lesson: always keep my devices out of reach of little explorers. If you ever find yourself in a similar predicament whether it’s tech-savvy toddlers or other digital disasters don’t hesitate to reach out to Digital Tech Guard Recovery. They saved my funds and my sanity, proving that no challenge is too great, even when it involves a toddler’s mischievous fingers!
21.11.24 08:02 Emily Hunter

If I hadn't found a review online and filed a complaint via email to support@deftrecoup. com , the people behind this unregulated scheme would have gotten away with leaving me in financial ruins. It was truly the most difficult period of my life.
22.11.24 04:41 [email protected]

I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
22.11.24 15:26 cliftonhandyman

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22.11.24 23:43 teresaborja

all thanks to Tech Cyber Force Recovery expert assistance. As a novice in cryptocurrency, I had been carefully accumulating a modest amount of Bitcoin, meticulously safeguarding my digital wallet and private keys. However, as the adage goes, the best-laid plans can often go awry, and that's precisely what happened to me. Due to a series of technical mishaps and human errors, I found myself locked out of my Bitcoin wallet, unable to access the fruits of my digital labors. Panic set in as I frantically searched for a solution, scouring the internet for any glimmer of hope. That's when I stumbled upon the Tech Cyber Force Recovery team, a group of seasoned cryptocurrency specialists who had built a reputation for their ability to recover lost or inaccessible digital assets. Skeptical at first, I reached out, desperate for a miracle. To my utter amazement, the Tech Cyber Force Recovery experts quickly assessed my situation and devised a meticulous plan of attack. Through their deep technical knowledge, unwavering determination, and a keen eye for detail, they were able to navigate the complex labyrinth of blockchain technology, ultimately recovering my entire Bitcoin portfolio. What had once seemed like a hopeless endeavor was now a reality, and I found myself once again in possession of my digital wealth, all thanks to the incredible efforts of the Tech Cyber Force Recovery team. This experience has not only restored my faith in the cryptocurrency ecosystem. Still, it has also instilled in me a profound appreciation for the critical role that expert recovery services can play in safeguarding one's digital assets. ENAIL < Tech cybers force recovery @ cyber services. com > WEBSITE < ht tps : // tech cyber force recovery. info > TEXT < +1. 561. 726. 3697 >
24.11.24 02:21 [email protected]

I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
25.11.24 02:19 briankennedy

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25.11.24 02:20 briankennedy

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26.11.24 21:59 [email protected]

In a world brimming with enticing investment opportunities, it is crucial to tread carefully. The rise of digital currencies has attracted many eager investors, but along with this excitement lurk deceitful characters ready to exploit the unsuspecting. I learned this lesson the hard way, and I want to share my story in the hopes that it can save someone from making the same mistakes I did. It all began innocently enough when I came across an engaging individual on Facebook. Lured in by promises of high returns in the cryptocurrency market, I felt the electric thrill of potential wealth coursing through me. Initial investments returned some profits, and that exhilarating taste of success fueled my ambition. Encouraged by a meager withdrawal, I decided to commit even more funds. This was the moment I let my guard down, blinded by greed. As time went on, the red flags started to multiply. The moment I tried to withdraw my earnings, a cascade of unreasonable fees appeared like a thick mist, obscuring the truth. “Just a little more,” they said, “Just until the next phase.” I watched my hard-earned money slip through my fingers as I scraped together every last cent to pay those relentless fees. My trust had become my downfall. In the end, I lost not just a significant amount of cash, but my peace of mind about $1.1 million vanished into the abyss of false promises and hollow guarantees. But despair birthed hope. After a cascade of letdowns, I enlisted the help of KAY-NINE CYBER SERVICES, a team that specializes in reclaiming lost funds from scams. Amazingly, they worked tirelessly to piece together what had been ripped away, providing me with honest guidance when I felt utterly defeated. Their expertise in navigating the treacherous waters of crypto recovery was a lifeline I desperately needed. To anyone reading this, please let my story serve as a warning. High returns often come wrapped in the guise of deception. Protect your investments, scrutinize every opportunity, and trust your instincts. Remember, the allure of quick riches can lead you straight to heartbreak, but with cautious determination and support, it is possible to begin healing from such devastating loss. Stay informed, stay vigilant, and may you choose your investment paths wisely. Email: kaynine @ cyberservices . com
26.11.24 23:12 rickrobinson8

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26.11.24 23:12 rickrobinson8

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00:39 [email protected]

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